# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------

from functools import partial

import torch
import torch.nn as nn

from timm.models.vision_transformer import PatchEmbed, Block

from util.pos_embed import get_2d_sincos_pos_embed



class MaskedAutoencoderViT(nn.Module):
    """ Masked Autoencoder with VisionTransformer backbone
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3,
                 embed_dim=1024, depth=24, num_heads=16,
                 decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
                 mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False):
        super().__init__()

        # --------------------------------------------------------------------------
        # MAE encoder specifics
        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False)  # fixed sin-cos embedding

        self.blocks = nn.ModuleList([
            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)
        # --------------------------------------------------------------------------

        # --------------------------------------------------------------------------
        # MAE decoder specifics
        self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)

        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))

        self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False)  # fixed sin-cos embedding

        self.decoder_blocks = nn.ModuleList([
            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
            for i in range(decoder_depth)])

        self.decoder_norm = norm_layer(decoder_embed_dim)
        self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch
        # --------------------------------------------------------------------------

        self.norm_pix_loss = norm_pix_loss

        self.initialize_weights()

    def initialize_weights(self):
        # initialization
        # initialize (and freeze) pos_embed by sin-cos embedding
        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))

        # initialize patch_embed like nn.Linear (instead of nn.Conv2d)
        w = self.patch_embed.proj.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.cls_token, std=.02)
        torch.nn.init.normal_(self.mask_token, std=.02)

        # initialize nn.Linear and nn.LayerNorm
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            # we use xavier_uniform following official JAX ViT:
            torch.nn.init.xavier_uniform_(m.weight)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def patchify(self, imgs):
        """
        imgs: (N, 3, H, W)
        x: (N, L, patch_size**2 *3)
        """
        p = self.patch_embed.patch_size[0]
        assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0

        h = w = imgs.shape[2] // p
        x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
        x = torch.einsum('nchpwq->nhwpqc', x)
        x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * 3))
        return x

    def unpatchify(self, x):
        """
        x: (N, L, patch_size**2 *3)
        imgs: (N, 3, H, W)
        """
        p = self.patch_embed.patch_size[0]
        h = w = int(x.shape[1]**.5)
        assert h * w == x.shape[1]
        
        x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
        x = torch.einsum('nhwpqc->nchpwq', x)
        imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p))
        return imgs

    def random_masking(self, x, mask_ratio):
        """
        Perform per-sample random masking by per-sample shuffling.
        Per-sample shuffling is done by argsort random noise.
        x: [N, L, D], sequence
        """
        N, L, D = x.shape  # batch, length, dim
        len_keep = int(L * (1 - mask_ratio))
        
        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
        
        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)

        # keep the first subset
        ids_keep = ids_shuffle[:, :len_keep]
        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore

    def forward_encoder(self, x, mask_ratio):
        # embed patches
        x = self.patch_embed(x)

        # add pos embed w/o cls token
        x = x + self.pos_embed[:, 1:, :]

        # masking: length -> length * mask_ratio
        x, mask, ids_restore = self.random_masking(x, mask_ratio)

        # append cls token
        cls_token = self.cls_token + self.pos_embed[:, :1, :]
        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        
        # apply Transformer blocks
        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)

        return x, mask, ids_restore

    def forward_decoder(self, x, ids_restore):
        # embed tokens
        x = self.decoder_embed(x)

        # append mask tokens to sequence
        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token
        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle
        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token

        # add pos embed
        x = x + self.decoder_pos_embed

        # apply Transformer blocks
        for blk in self.decoder_blocks:
            x = blk(x)
        x = self.decoder_norm(x)

        # predictor projection
        x = self.decoder_pred(x)

        # remove cls token
        x = x[:, 1:, :]

        return x

    def forward_loss(self, imgs, pred, mask):
        """
        imgs: [N, 3, H, W]
        pred: [N, L, p*p*3]
        mask: [N, L], 0 is keep, 1 is remove, 
        """
        target = self.patchify(imgs)
        if self.norm_pix_loss:
            mean = target.mean(dim=-1, keepdim=True)
            var = target.var(dim=-1, keepdim=True)
            target = (target - mean) / (var + 1.e-6)**.5

        loss = (pred - target) ** 2
        loss = loss.mean(dim=-1)  # [N, L], mean loss per patch

        loss = (loss * mask).sum() / mask.sum()  # mean loss on removed patches
        return loss

    def forward(self, imgs, mask_ratio=0.75, adv_images=None):
        # readv pre-training
        # if adv_images is not None:
        #     latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio)
        #     pred = self.forward_decoder(latent, ids_restore)  # [N, L, p*p*3]
        #     loss = self.forward_loss(adv_images, pred, mask)
        #     return loss, pred, mask

        # adv pre-training
        if adv_images is not None:
            latent, mask, ids_restore = self.forward_encoder(adv_images, mask_ratio)
            pred = self.forward_decoder(latent, ids_restore)  # [N, L, p*p*3]
            loss = self.forward_loss(imgs, pred, mask)
            return loss, pred, mask, latent

        latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio)
        pred = self.forward_decoder(latent, ids_restore)  # [N, L, p*p*3]
        loss = self.forward_loss(imgs, pred, mask)
        return loss, pred, mask, latent


class LayerNorm(nn.Module):
    r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. 
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with 
    shape (batch_size, height, width, channels) while channels_first corresponds to inputs 
    with shape (batch_size, channels, height, width).
    """
    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_first"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError 
        self.normalized_shape = (normalized_shape, )
    
    def forward(self, x):
        if self.data_format == "channels_last":
            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        elif self.data_format == "channels_first":
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
            return x


class ConvBlock(nn.Module):
    expansion = 1
    def __init__(self, siz=48, end_siz=8, fin_dim=384):
        super(ConvBlock, self).__init__()
        self.planes = siz
        fin_dim = self.planes*end_siz if fin_dim != 432 else 432
        # self.bn = nn.BatchNorm2d(planes) if self.normaliz == "bn" else nn.GroupNorm(num_groups=1, num_channels=planes)
        self.stem = nn.Sequential(nn.Conv2d(3, self.planes, kernel_size=3, stride=2, padding=1),
                                  LayerNorm(self.planes, data_format="channels_first"),
                                  nn.GELU(),
                                  nn.Conv2d(self.planes, self.planes*2, kernel_size=3, stride=2, padding=1),
                                  LayerNorm(self.planes*2, data_format="channels_first"),
                                  nn.GELU(),
                                  nn.Conv2d(self.planes*2, self.planes*4, kernel_size=3, stride=2, padding=1),
                                  LayerNorm(self.planes*4, data_format="channels_first"),
                                  nn.GELU(),
                                  nn.Conv2d(self.planes*4, self.planes*8, kernel_size=3, stride=2, padding=1),
                                  LayerNorm(self.planes*8, data_format="channels_first"),
                                  nn.GELU(),
                                  nn.Conv2d(self.planes*8, fin_dim, kernel_size=1, stride=1, padding=0)
                        )
    def forward(self, x):
        out = self.stem(x)
        # out = self.bn(out)
        return out


smaller_decoder = {
    'decoder_embed_dim': 128,
    'decoder_depth': 2,
    'decoder_num_heads': 16
}

def mae_vit_ti_dec128d2b(**kwargs):
    model = MaskedAutoencoderViT(
        embed_dim=192, depth=12, num_heads=3,
        decoder_embed_dim=smaller_decoder['decoder_embed_dim'], 
        decoder_depth=smaller_decoder['decoder_depth'], 
        decoder_num_heads=smaller_decoder['decoder_num_heads'],
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model

def mae_vit_small_dec128d2b(**kwargs):
    model = MaskedAutoencoderViT(
        embed_dim=384, depth=12, num_heads=6,
        decoder_embed_dim=smaller_decoder['decoder_embed_dim'], 
        decoder_depth=smaller_decoder['decoder_depth'], 
        decoder_num_heads=smaller_decoder['decoder_num_heads'],
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model

def mae_vit_small_cvb_dec128d2b(**kwargs):
    model = MaskedAutoencoderViT(
        embed_dim=384, depth=12, num_heads=6,
        decoder_embed_dim=smaller_decoder['decoder_embed_dim'], 
        decoder_depth=smaller_decoder['decoder_depth'], 
        decoder_num_heads=smaller_decoder['decoder_num_heads'],
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.patch_embed.proj = ConvBlock(48, end_siz=8)
    return model


def mae_vit_base_cvb_dec128d2b(**kwargs):
    model = MaskedAutoencoderViT(
        embed_dim=768, depth=12, num_heads=12,
        decoder_embed_dim=smaller_decoder['decoder_embed_dim'], 
        decoder_depth=smaller_decoder['decoder_depth'], 
        decoder_num_heads=smaller_decoder['decoder_num_heads'],
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.patch_embed.proj = ConvBlock(48, end_siz=16, fin_dim=None)
    return model


def mae_vit_small_ds_dec128d2b(**kwargs):
    model = MaskedAutoencoderViT(
        embed_dim=768, depth=8, num_heads=8,
        decoder_embed_dim=smaller_decoder['decoder_embed_dim'], 
        decoder_depth=smaller_decoder['decoder_depth'], 
        decoder_num_heads=smaller_decoder['decoder_num_heads'],
        mlp_ratio=3, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model

def mae_vit_base_dec128d2b(**kwargs):
    model = MaskedAutoencoderViT(
        embed_dim=768, depth=12, num_heads=12,
        decoder_embed_dim=smaller_decoder['decoder_embed_dim'], 
        decoder_depth=smaller_decoder['decoder_depth'], 
        decoder_num_heads=smaller_decoder['decoder_num_heads'],
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model

def mae_vit_large_dec128d2b(**kwargs):
    model = MaskedAutoencoderViT(
        embed_dim=1024, depth=24, num_heads=16,
        decoder_embed_dim=smaller_decoder['decoder_embed_dim'], 
        decoder_depth=smaller_decoder['decoder_depth'], 
        decoder_num_heads=smaller_decoder['decoder_num_heads'],
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


# set recommended archs
mae_vit_small_ds=mae_vit_small_ds_dec128d2b
mae_vit_ti=mae_vit_ti_dec128d2b
mae_vit_small=mae_vit_small_dec128d2b
mae_vit_small_cvb=mae_vit_small_cvb_dec128d2b
mae_vit_base=mae_vit_base_dec128d2b
mae_vit_base_cvb=mae_vit_base_cvb_dec128d2b
mae_vit_large=mae_vit_large_dec128d2b

